import math
import cmath
import numpy as np

#君威车轮实际数据

data_select = 0

dataset =[
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    }
]

front_buick = dataset[data_select]

front = {
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    "angle_increment": 0.00581718236208,
    "time_increment": 6.17283949396e-05,
    "scan_time": 0.0666666701436,
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}

def wheel_edge_detect(scan, wheel_distance_threshold = 3.0, cnt_threshold = 5):
    '''
    说明
    =======================================
    从中心开始往两边找车轮

        - param scan
        雷达数据

        - param wheel_distance_threshold
        小于这个距离的数据才进行分析

        - param cnt_threshold
        连续找到这个数量符合要求的数据认为找到车轮边缘

        - return 返回一组索引值[左边缘，右边缘]
    '''

    def distance(p1, p2):
        return math.sqrt( (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2 )

    def in_scope(val, min=0, max=100):
        return val >= min and val < max

    mid_index = math.floor(len(scan['ranges'])/2)
    left_max = mid_index
    right_max = len(scan['ranges']) - mid_index - 1

    wheel_distance = 0

    # 从中间向两边开始找
    left = mid_index
    right = mid_index
    left_detected = False
    right_detected = False
    # 左边计数
    left_cnt = 0
    # 右边计数
    right_cnt = 0

    detect_range = left_max if left_max <= right_max else right_max

    for i in range(detect_range):
        if i != 0:
            if not left_detected:
                if scan['ranges'][mid_index - i] < wheel_distance_threshold:
                    left = mid_index - i
                    left_cnt += 1
                else:
                    left_cnt = 0
                
                if left_cnt > cnt_threshold: left_detected = True

            if not right_detected:
                if scan['ranges'][mid_index + i] < wheel_distance_threshold:
                    right = mid_index + i
                    right_cnt += 1
                else:
                    right_cnt = 0
                
                if right_cnt > cnt_threshold: right_detected = True

            #都找到后判断一下距离是否符合1.2~1.8的车体宽度
            if left_detected and right_detected:
                left_point = pollar2cartesian(scan['ranges'][left], scan['angles'][left])
                right_point = pollar2cartesian(scan['ranges'][right], scan['angles'][right])
                wheel_distance = distance(left_point, right_point)
                if not in_scope(wheel_distance, min=1.2, max=1.8):
                    if scan['ranges'][left] > scan['ranges'][right]:
                        left_detected = False
                        left_cnt = 0
                    else:
                        right_detected = False
                        right_cnt = 0

            #end for


    if left_detected and right_detected:
        '''
        [ left(right wheel), right(left wheel) ]
        '''
        print('wheel distance %.2f'%wheel_distance)
        return [left, right]
    else:
        return [0, 0]

def pollar2cartesian(*scan_pollar):
    '''
    极坐标转直角坐标
    ===============

    - param scan_pollar
        (r, phi)极坐标点
    '''
    cn = cmath.rect(*scan_pollar)
    return (cn.real, cn.imag)

def get_obstacle_array(scan):
    '''
    得到所有的障碍物点（直角坐标系）
    '''
    print(scan)

    data_len = len(scan['ranges'])
    ob_list = list()

    for i in range(data_len):
        ob_list.append(list(pollar2cartesian(
            scan['ranges'][i],
            scan['angles'][i]
        )))

    return np.array(ob_list)

def mps2rpm(vel):
    '''
    m/s转换为电机rpm
    '''
    wheel_diameter = 0.1
    speed_radio = 85 / 29
    rpm = math.floor(vel * speed_radio / (math.pi * wheel_diameter) * 60)
    print("[%.2fm/s]->[%drpm]"%(vel,rpm))
    return rpm

if __name__ == '__main__':
    mps2rpm(0.2)
    mps2rpm(0.01)

